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Deep Hyperalignment
Muhammad Yousefnezhad · Daoqiang Zhang

Thu Dec 07 12:05 PM -- 12:10 PM (PST) @ Hall A

This paper proposes Deep Hyperalignment (DHA) as a regularized, deep extension, scalable Hyperalignment (HA) method, which is well-suited for applying functional alignment to fMRI datasets with nonlinearity, high-dimensionality (broad ROI), and a large number of subjects. Unlink previous methods, DHA is not limited by a restricted fixed kernel function. Further, it uses a parametric approach, rank-m Singular Value Decomposition (SVD), and stochastic gradient descent for optimization. Consequently, the time complexity of DHA fairly scales with data size and the training data is not referenced when DHA computes the functional alignment for a new subject. Experimental studies on multi-subject fMRI analysis confirm that the DHA method achieves superior performance to other state-of-the-art HA algorithms.

Author Information

Muhammad Yousefnezhad (Nanjing University of Aeronautics and Astronautics)

I am the Director of Brain Decoding section, iBRAIN group, Department of Computer Science & Technology, Nanjing University of Aeronautics and Astronautics. We are developing Artificial Intelligence algorithms in order to understand (decode) generated patterns in the human brain. Most of my counterparts try to change the world, but I am first trying to understand how it works!

Daoqiang Zhang (Nanjing University of Aeronautics and Astronautics)

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